Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By : Nick McClure, Sujit Pal
Book Image

TensorFlow Machine Learning Cookbook - Second Edition

By: Nick McClure, Sujit Pal

Overview of this book

TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and allow you to dig deeper and gain more insights into your data than ever before. With the help of this book, you will work with recipes for training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and more. You will explore RNNs, CNNs, GANs, reinforcement learning, and capsule networks, each using Google's machine learning library, TensorFlow. Through real-world examples, you will get hands-on experience with linear regression techniques with TensorFlow. Once you are familiar and comfortable with the TensorFlow ecosystem, you will be shown how to take it to production. By the end of the book, you will be proficient in the field of machine intelligence using TensorFlow. You will also have good insight into deep learning and be capable of implementing machine learning algorithms in real-world scenarios.
Table of Contents (13 chapters)

Using nearest-neighbors for image recognition

Nearest-neighbors can also be used for image recognition. The hello world of image recognition datasets is the MNIST handwritten digit dataset. Since we will be using this dataset for various neural network image recognition algorithms in later chapters, it will be great to compare the results to a non-neural network algorithm.

Getting ready

The MNIST digit dataset is composed of thousands of labeled images that are 28x28 pixels in size. Although this is considered a small image, it has a total of 784 pixels (or features) for the nearest-neighbor algorithm. We will compute the nearest-neighbor prediction for this categorical problem by considering the mode prediction of the nearest...